Moving Toward A Digital Competency-based Approach in Applied Education: Developing a System Supported by Blockchain to Enhance Competency-Based Credentials
Bibliographic record
Abstract
A competency-based approach to education (CBE) has emerged in recent years, fostering curriculum by tracking and indicting students' acquired skills and competencies. Since applied education is moving away from a theoretical approach to its application, employers are eager to be empowered with graduates' full e-profiles, which demonstrate candidates' competency-based strengths and weaknesses. This study considered a new digital system for competency-based learning, enhanced by Blockchain and badge technologies, to improve and indicate practical classes' quality in applied programs. Our core objectives were to promote the digitalization of competency-based education and students' e-portfolios as a proposed system in applied education. We also assessed its implementation, beginning with a learning gap analysis and moving on to discuss the digital CBE to support employers' ability to validate graduates' competency-based credentials acquired through their signature learning experience. We found that the digitalization of skills and competency-based credentials should be enhanced to foster knowing-by-doing and practical capabilities, which should be incorporated in applied education to achieve optimum CBE results and support recruitment and professional development processes. Further research and study are recommended to develop and unify standards adopted by the Higher Education Institutions (HEIs), that are recognized by the industries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".